Stack Overflow in Echo Go with Cockroachdb

A stack overflow in an Echo Go service backed by Cockroachdb typically originates from unbounded recursion or deep call chains in request handling, and the database interaction can amplify the impact. When an Echo handler performs recursive or deeply nested operations—such as walking a tree structure stored in Cockroachdb without a depth limit—the stack may grow until it overflows. Because Cockroachdb often serves as the source of hierarchical data (e.g., org charts, category trees, or comment threads), unbounded traversal of rows that form deep or cyclic graphs creates a path to stack exhaustion.

This combination also exposes the vulnerability through reflection-based routing and context propagation in Echo. If middleware or handlers recursively invoke downstream services or retry logic while holding large context objects, the stack grows further. Cockroachdb’s wire protocol and Go drivers do not inherently prevent deep recursion; they simply return rows that a developer may process recursively. Without iterative traversal or explicit depth controls, an attacker can craft requests that trigger deep or cyclic data access, leading to a stack overflow that crashes the service. Unlike some runtimes that provide guard pages or configurable stack sizes, a Go service using Echo and Cockroachdb relies on developer discipline to bound recursion and iteration.

An example scenario: an endpoint like /org/{id}/tree queries Cockroachdb for a subtree using a recursive common table expression (CTE) or iterative fan-out in Go. If the data depth is uncontrolled and the handler recurses over each child node, a deep or maliciously crafted hierarchy can overflow the stack. Because the scan is unauthenticated in middleBrick’s black-box methodology, an attacker can probe this endpoint without credentials, and a stack overflow may be triggered as one of the runtime findings under unsafe consumption or input validation checks. This highlights the importance of bounding traversal depth and avoiding recursive handlers when integrating Echo, Cockroachdb, and user-supplied identifiers.

To remediate stack overflow risks, avoid recursive traversal in handlers and enforce explicit depth limits when walking hierarchical data from Cockroachdb. Use iterative approaches with an explicit stack or queue, and bound the depth of traversal. The following examples show a safe pattern for querying a hierarchical table in Cockroachdb using Go with the lib/pq driver and Echo, avoiding unbounded recursion.

// Safe iterative traversal with explicit depth limit
package main

import (
    "context"
    "net/http"
    "github.com/labstack/echo/v4"
    "github.com/lib/pq"
    "database/sql"
    _ "github.com/lib/pq"
)

type Node struct {
    ID   int64
    Name string
}

// maxDepth prevents stack overflow by bounding traversal
const maxDepth = 20

func getSubtree(c echo.Context) error {
    db, ok := c.Get("db").(*sql.DB)
    if !ok {
        return c.String(http.StatusInternalServerError, "db unavailable")
    }
    rootID := c.Param("id")

    // Use a queue for breadth-first iterative traversal with depth tracking
    type queueItem struct {
        id     string
        depth  int
    }
    queue := []queueItem{{id: rootID, depth: 0}}
    visited := make(map[string]bool)
    var results []Node

    for len(queue) > 0 {
        item := queue[0]
        queue = queue[1:]
        if item.depth > maxDepth {
            continue // skip deeper nodes to prevent overflow
        }
        if visited[item.id] {
            continue // avoid cycles
        }
        visited[item.id] = true

        var name string
        err := db.QueryRowContext(c.Request().Context(),
            "SELECT name FROM nodes WHERE id = $1", item.id,
        ).Scan(&name)
        if err != nil {
            if err == sql.ErrNoRows {
                continue
            }
            return c.String(http.StatusInternalServerError, "db error")
        }
        results = append(results, Node{ID: parseID(item.id), Name: name})

        // Enqueue children iteratively; assumes a children table or array column
        var childIDs []string
        rows, err := db.QueryContext(c.Request().Context(),
            "SELECT child_id FROM node_children WHERE parent_id = $1", item.id,
        )
        if err != nil {
            return c.String(http.StatusInternalServerError, "db error")
        }
        for rows.Next() {
            var cid string
            if err := rows.Scan(&cid); err != nil {
                rows.Close()
                return c.String(http.StatusInternalServerError, "scan error")
            }
            childIDs = append(childIDs, cid)
        }
        rows.Close()
        for _, cid := range childIDs {
            if !visited[cid] {
                queue = append(queue, queueItem{id: cid, depth: item.depth + 1})
            }
        }
    }
    return c.JSON(http.StatusOK, results)
}

func main() {
    db, err := sql.Open("postgres", "user=app dbname=appdb sslmode=disable")
    if err != nil {
        panic(err)
    }
    defer db.Close()

    e := echo.New()
    e.GET("/org/:id/tree", getSubtree)
    e.Start(":8080")
}

Key practices: replace recursion with an explicit queue or stack, enforce a maxDepth constant, track visited nodes to avoid cycles, and use context-aware queries so Cockroachdb can respect timeouts. This approach eliminates stack growth from deep recursion and aligns with how an API security scanner like middleBrick may surface unsafe consumption findings—by detecting patterns that risk resource exhaustion.

Additionally, validate and sanitize incoming identifiers before use, and prefer parameterized queries to avoid injection. For production, couple this with middleware that monitors resource usage and fails safely rather than crashing. middleBrick’s checks for unsafe consumption and input validation can help surface such patterns early, and integrating the CLI (middlebrick scan <url>) or GitHub Action can enforce continuous monitoring of these risks in your CI/CD pipeline.